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Integrated Conditional Moment testing of quantile regression models

Author

Listed:
  • Herman J. Bierens

    () (Department of Economics, Pennsylvania State University, 608 Kern Graduate Building, University Park, PA16802, and Tilburg University, The Netherlands Research Department, Federal Reserve Bank of Atlanta, 104 Marietta Street, NW, Atlanta, GA 30303, USA)

  • Donna K. Ginther

    () (Department of Economics, Pennsylvania State University, 608 Kern Graduate Building, University Park, PA16802, and Tilburg University, The Netherlands Research Department, Federal Reserve Bank of Atlanta, 104 Marietta Street, NW, Atlanta, GA 30303, USA)

Abstract

In this paper we propose a consistent test of the linearity of quantile regression models, similar to the Integrated Conditional Moment (ICM) test of Bierens (1982) and Bierens and Ploberger (1997). This test requires re-estimation of the quantile regression model by minimizing the ICM test statistic with respect to the parameters. We apply this ICM test to examine the correctness of the functional form of three median regression wage equations.

Suggested Citation

  • Herman J. Bierens & Donna K. Ginther, 2001. "Integrated Conditional Moment testing of quantile regression models," Empirical Economics, Springer, vol. 26(1), pages 307-324.
  • Handle: RePEc:spr:empeco:v:26:y:2001:i:1:p:307-324
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    Cited by:

    1. Jhon James Mora, 2003. "Sheepskin effects and screening in Colombia," COLOMBIAN ECONOMIC JOURNAL, UN - RCE - CID, April.
      • Jhon James Mora, 2003. "Sheepskin effects and screening in Colombia," Colombian Economic Journal, Academia Colombiana de Ciencias Economicas, Colegio Mayor de Nuestra Senora del Rosario, Pontificia Universidad Javeriana, Universidad de Antioquia, Universidad de los Andes, Universidad del Valle, Universidad Externado de Colombia, Universidad Nacional de Colombia, vol. 1(1), pages 95-108, December.
    2. Komunjer, Ivana, 2005. "Quasi-maximum likelihood estimation for conditional quantiles," Journal of Econometrics, Elsevier, vol. 128(1), pages 137-164, September.
    3. Giacomini, Raffaella & Komunjer, Ivana, 2005. "Evaluation and Combination of Conditional Quantile Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 416-431, October.
    4. Kostov, Philip & Patton, Myles & Moss, Joan E. & McErlean, Seamus, 2005. "Does Gibrat's Law Hold Amongst Dairy Farmers in Northern Ireland?," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24775, European Association of Agricultural Economists.
    5. Christoph Rothe & Dominik Wied, 2013. "Misspecification Testing in a Class of Conditional Distributional Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 314-324, March.
    6. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, Elsevier.
    7. Horowitz, Joel L. & Spokoiny, Vladimir G., 2000. "An Adaptive, Rate-Optimal Test of Linearity for Median Regression Models," Working Papers 00-04, University of Iowa, Department of Economics.
    8. Sun, Yiguo, 2006. "A Consistent Nonparametric Equality Test Of Conditional Quantile Functions," Econometric Theory, Cambridge University Press, vol. 22(04), pages 614-632, August.
    9. Lawrence Dacuycuy, 2006. "Explaining male wage inequality in the Philippines: non-parametric and semiparametric approaches," Applied Economics, Taylor & Francis Journals, vol. 38(21), pages 2497-2511.
    10. Escanciano, Juan Carlos & Velasco, Carlos, 2010. "Specification tests of parametric dynamic conditional quantiles," Journal of Econometrics, Elsevier, vol. 159(1), pages 209-221, November.
    11. repec:hal:journl:peer-00732534 is not listed on IDEAS
    12. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
    13. Escanciano, J.C. & Goh, S.C., 2014. "Specification analysis of linear quantile models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 495-507.
    14. Lajos Zoltán Bakucs & Imre Fertő, 2009. "The growth of family farms in Hungary," Agricultural Economics, International Association of Agricultural Economists, vol. 40(s1), pages 789-795, November.
    15. Juan Carlos Escanciano & Chuan Goh, 2010. "Specification Analysis of Structural Quantile Regression Models," Working Papers tecipa-415, University of Toronto, Department of Economics.
    16. Latruffe, Laure & Desjeux, Yann & Fogarasi, Jozsef & Bakucs, Lajos Zoltan & Ferto, Imre, 2010. "Technical efficiency and environmental pressures of pig farms in Hungary," 120th Seminar, September 2-4, 2010, Chania, Crete 109385, European Association of Agricultural Economists.
    17. Fertő, Imre & Bakucs, Lajos Zoltán, 2008. "Érvényes-e a Gibrat-törvény a magyar mezőgazdaságban?
      [Is Gibrat s law valid for Hungarian agriculture?]
      ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(1), pages 25-38.
    18. Komunjer, Ivana & Vuong, Quang, 2010. "Efficient estimation in dynamic conditional quantile models," Journal of Econometrics, Elsevier, vol. 157(2), pages 272-285, August.

    More about this item

    Keywords

    Quantile regression; Test for linearity; Integrated conditional moment test; Wage equations;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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